Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.3 KiB
Average record size in memory144.0 B

Variable types

Categorical8
Numeric10

Alerts

calidad_pm25 has constant value "1.0"Constant
codigoserial is uniformly distributedUniform
presion has 57 (31.7%) zerosZeros
p1 has 63 (35.0%) zerosZeros

Reproduction

Analysis started2024-08-22 15:26:49.975768
Analysis finished2024-08-22 15:27:06.119554
Duration16.14 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

mes
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
93 
2
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Length

2024-08-22T10:27:06.254416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:06.427308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring characters

ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

dia
Real number (ℝ)

Distinct31
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.516667
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-08-22T10:27:06.610696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15.5
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7084849
Coefficient of variation (CV)0.56123426
Kurtosis-1.1864689
Mean15.516667
Median Absolute Deviation (MAD)7.5
Skewness0.011522316
Sum2793
Variance75.837709
MonotonicityNot monotonic
2024-08-22T10:27:06.802437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 6
 
3.3%
2 6
 
3.3%
29 6
 
3.3%
28 6
 
3.3%
27 6
 
3.3%
26 6
 
3.3%
25 6
 
3.3%
24 6
 
3.3%
23 6
 
3.3%
22 6
 
3.3%
Other values (21) 120
66.7%
ValueCountFrequency (%)
1 6
3.3%
2 6
3.3%
3 6
3.3%
4 6
3.3%
5 6
3.3%
6 6
3.3%
7 6
3.3%
8 6
3.3%
9 6
3.3%
10 6
3.3%
ValueCountFrequency (%)
31 3
1.7%
30 3
1.7%
29 6
3.3%
28 6
3.3%
27 6
3.3%
26 6
3.3%
25 6
3.3%
24 6
3.3%
23 6
3.3%
22 6
3.3%

pm25
Real number (ℝ)

Distinct179
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.91051
Minimum-1235.5337
Maximum16692.348
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)5.6%
Memory size2.8 KiB
2024-08-22T10:27:07.007220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1235.5337
5-th percentile-389.33758
Q121.115891
median29.484673
Q336.236144
95-th percentile4185.2472
Maximum16692.348
Range17927.882
Interquartile range (IQR)15.120253

Descriptive statistics

Standard deviation1867.3281
Coefficient of variation (CV)4.6810701
Kurtosis42.74711
Mean398.91051
Median Absolute Deviation (MAD)7.6909042
Skewness5.9999492
Sum71803.892
Variance3486914.1
MonotonicityNot monotonic
2024-08-22T10:27:07.230903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.04166667 2
 
1.1%
20.46693333 1
 
0.6%
-395.235265 1
 
0.6%
19.12517083 1
 
0.6%
-814.26774 1
 
0.6%
22.30719125 1
 
0.6%
23.05252042 1
 
0.6%
26.29606667 1
 
0.6%
26.01087083 1
 
0.6%
18.8345425 1
 
0.6%
Other values (169) 169
93.9%
ValueCountFrequency (%)
-1235.533741 1
0.6%
-1234.117461 1
0.6%
-821.8938267 1
0.6%
-820.9419133 1
0.6%
-814.26774 1
0.6%
-402.1138379 1
0.6%
-401.103565 1
0.6%
-395.235265 1
0.6%
-391.8376 1
0.6%
-389.2060042 1
0.6%
ValueCountFrequency (%)
16692.34784 1
0.6%
12510.25325 1
0.6%
7524.074892 1
0.6%
4206.857683 1
0.6%
4202.691975 1
0.6%
4197.800408 1
0.6%
4197.21365 1
0.6%
4195.172246 1
0.6%
4188.727058 1
0.6%
4185.064037 1
0.6%

calidad_pm25
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
1.0
180 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters540
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 180
100.0%

Length

2024-08-22T10:27:07.447757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:07.600137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 180
100.0%

Most occurring characters

ValueCountFrequency (%)
1 180
33.3%
. 180
33.3%
0 180
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 180
33.3%
. 180
33.3%
0 180
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 180
33.3%
. 180
33.3%
0 180
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 180
33.3%
. 180
33.3%
0 180
33.3%

codigoserial
Categorical

UNIFORM 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
28
60 
69
60 
86
60 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters360
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28

Common Values

ValueCountFrequency (%)
28 60
33.3%
69 60
33.3%
86 60
33.3%

Length

2024-08-22T10:27:07.763954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:07.922897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
28 60
33.3%
69 60
33.3%
86 60
33.3%

Most occurring characters

ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

temperatura
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.432939
Minimum-999
Maximum25.805556
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)7.2%
Memory size2.8 KiB
2024-08-22T10:27:08.110149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q121.149672
median22.569056
Q324.01053
95-th percentile25.146597
Maximum25.805556
Range1024.8056
Interquartile range (IQR)2.8608577

Descriptive statistics

Standard deviation236.97162
Coefficient of variation (CV)-6.3305643
Kurtosis12.689623
Mean-37.432939
Median Absolute Deviation (MAD)1.4451632
Skewness-3.7976194
Sum-6737.929
Variance56155.551
MonotonicityNot monotonic
2024-08-22T10:27:08.329186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
21.20336811 1
 
0.6%
22.43326389 1
 
0.6%
-487.1159722 1
 
0.6%
21.16861111 1
 
0.6%
22.01006944 1
 
0.6%
21.27694444 1
 
0.6%
21.59708333 1
 
0.6%
21.754375 1
 
0.6%
22.10930556 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-487.1159722 1
 
0.6%
-30.52168743 1
 
0.6%
-2.906805556 1
 
0.6%
13.58625 1
 
0.6%
17.50569444 1
 
0.6%
17.86701389 1
 
0.6%
18.04083333 1
 
0.6%
18.73861111 1
 
0.6%
18.8175 1
 
0.6%
ValueCountFrequency (%)
25.80555556 1
0.6%
25.52388889 1
0.6%
25.51756944 1
0.6%
25.50707636 1
0.6%
25.49993056 1
0.6%
25.49090278 1
0.6%
25.42810416 1
0.6%
25.39229167 1
0.6%
25.38145833 1
0.6%
25.13423608 1
0.6%
Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
109 
155
60 
151
11 

Length

Max length3
Median length1
Mean length1.7888889
Min length1

Characters and Unicode

Total characters322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row155
2nd row155
3rd row155
4th row155
5th row155

Common Values

ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Length

2024-08-22T10:27:08.530848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:08.703329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Most occurring characters

ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

humedad
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7089607
Minimum-999
Maximum84.515444
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-08-22T10:27:08.892839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q160.754462
median67.10941
Q374.028226
95-th percentile79.726716
Maximum84.515444
Range1083.5154
Interquartile range (IQR)13.273764

Descriptive statistics

Standard deviation247.62645
Coefficient of variation (CV)43.375049
Kurtosis12.680085
Mean5.7089607
Median Absolute Deviation (MAD)6.7010069
Skewness-3.7943879
Sum1027.6129
Variance61318.859
MonotonicityNot monotonic
2024-08-22T10:27:09.112294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
84.51544444 1
 
0.6%
74.615 1
 
0.6%
-457.6452083 1
 
0.6%
82.33111111 1
 
0.6%
77.93104167 1
 
0.6%
77.36708333 1
 
0.6%
74.94527778 1
 
0.6%
73.88020833 1
 
0.6%
71.14763889 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-457.6452083 1
 
0.6%
17.6589931 1
 
0.6%
31.39416667 1
 
0.6%
54.21333333 1
 
0.6%
54.94104167 1
 
0.6%
55.531875 1
 
0.6%
55.66118056 1
 
0.6%
56.40430556 1
 
0.6%
56.65270833 1
 
0.6%
ValueCountFrequency (%)
84.51544444 1
0.6%
84.4698125 1
0.6%
83.79554867 1
0.6%
83.44047228 1
0.6%
82.77643755 1
0.6%
82.33111111 1
0.6%
81.34544446 1
0.6%
80.49526392 1
0.6%
79.72923611 1
0.6%
79.7265833 1
0.6%

calidad_humedad
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
109 
155
60 
151
11 

Length

Max length3
Median length1
Mean length1.7888889
Min length1

Characters and Unicode

Total characters322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row155
2nd row155
3rd row155
4th row155
5th row155

Common Values

ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Length

2024-08-22T10:27:09.508445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:09.681901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Most occurring characters

ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

presion
Real number (ℝ)

ZEROS 

Distinct114
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.74671
Minimum-999
Maximum853.8609
Zeros57
Zeros (%)31.7%
Negative14
Negative (%)7.8%
Memory size2.8 KiB
2024-08-22T10:27:09.870493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median825.43719
Q3850.27476
95-th percentile852.5442
Maximum853.8609
Range1852.8609
Interquartile range (IQR)850.27476

Descriptive statistics

Standard deviation528.43086
Coefficient of variation (CV)1.1697503
Kurtosis0.60645694
Mean451.74671
Median Absolute Deviation (MAD)26.514375
Skewness-1.1449589
Sum81314.407
Variance279239.17
MonotonicityNot monotonic
2024-08-22T10:27:10.085445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
31.7%
-999 10
 
5.6%
-1.3875 2
 
1.1%
852.4464583 1
 
0.6%
850.2206944 1
 
0.6%
850.9357639 1
 
0.6%
851.6726389 1
 
0.6%
853.2250694 1
 
0.6%
853.8609028 1
 
0.6%
852.475625 1
 
0.6%
Other values (104) 104
57.8%
ValueCountFrequency (%)
-999 10
 
5.6%
-80.75256944 1
 
0.6%
-52.03125 1
 
0.6%
-1.3875 2
 
1.1%
0 57
31.7%
798.1502778 1
 
0.6%
814.9458333 1
 
0.6%
824.0225 1
 
0.6%
824.0748611 1
 
0.6%
824.114375 1
 
0.6%
ValueCountFrequency (%)
853.8609028 1
0.6%
853.2250694 1
0.6%
853.1511806 1
0.6%
853.0895139 1
0.6%
852.8140972 1
0.6%
852.7811806 1
0.6%
852.7346528 1
0.6%
852.681875 1
0.6%
852.6081944 1
0.6%
852.5408333 1
0.6%

calidad_presion
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
109 
155
60 
151
11 

Length

Max length3
Median length1
Mean length1.7888889
Min length1

Characters and Unicode

Total characters322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row155
2nd row155
3rd row155
4th row155
5th row155

Common Values

ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Length

2024-08-22T10:27:10.299993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:10.468837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Most occurring characters

ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

p1
Real number (ℝ)

ZEROS 

Distinct50
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-116.92111
Minimum-999
Maximum0.064527778
Zeros63
Zeros (%)35.0%
Negative41
Negative (%)22.8%
Memory size2.8 KiB
2024-08-22T10:27:10.655777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median0
Q30.00014409722
95-th percentile0.017636806
Maximum0.064527778
Range999.06453
Interquartile range (IQR)0.00014409722

Descriptive statistics

Standard deviation317.05482
Coefficient of variation (CV)-2.7116987
Kurtosis3.9114326
Mean-116.92111
Median Absolute Deviation (MAD)5.2083333 × 10-5
Skewness-2.408165
Sum-21045.8
Variance100523.76
MonotonicityNot monotonic
2024-08-22T10:27:10.879866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
35.0%
-999 20
 
11.1%
1.388888889 × 10-512
 
6.7%
6.944444444 × 10-610
 
5.6%
0.001791666667 3
 
1.7%
0.000125 3
 
1.7%
3.472222222 × 10-53
 
1.7%
0.009680555556 2
 
1.1%
0.002388888889 2
 
1.1%
0.007159722222 2
 
1.1%
Other values (40) 60
33.3%
ValueCountFrequency (%)
-999 20
11.1%
-496.725 2
 
1.1%
-27.75 1
 
0.6%
-6.243708333 2
 
1.1%
-6.242159722 1
 
0.6%
-5.549923611 1
 
0.6%
-4.1625 1
 
0.6%
-2.08125 1
 
0.6%
-2.081243056 1
 
0.6%
-2.079875 1
 
0.6%
ValueCountFrequency (%)
0.06452777778 2
1.1%
0.04209027778 2
1.1%
0.03158333333 2
1.1%
0.02457638889 1
0.6%
0.02313888889 2
1.1%
0.01734722222 2
1.1%
0.01594444444 1
0.6%
0.01265277778 2
1.1%
0.009680555556 2
1.1%
0.007159722222 2
1.1%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
158 
151
22 

Length

Max length3
Median length1
Mean length1.2444444
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 158
87.8%
151 22
 
12.2%

Length

2024-08-22T10:27:11.160566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:11.349315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 158
87.8%
151 22
 
12.2%

Most occurring characters

ValueCountFrequency (%)
1 202
90.2%
5 22
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 202
90.2%
5 22
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 202
90.2%
5 22
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 202
90.2%
5 22
 
9.8%

velocidad_prom
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-57.11957
Minimum-999
Maximum3.4436111
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)11.1%
Memory size2.8 KiB
2024-08-22T10:27:11.543017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.5083333
median1.790691
Q32.0823125
95-th percentile2.5759441
Maximum3.4436111
Range1002.4436
Interquartile range (IQR)0.57397917

Descriptive statistics

Standard deviation232.09903
Coefficient of variation (CV)-4.0633889
Kurtosis12.694335
Mean-57.11957
Median Absolute Deviation (MAD)0.29175347
Skewness-3.798311
Sum-10281.523
Variance53869.959
MonotonicityNot monotonic
2024-08-22T10:27:11.774706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
1.911458333 1
 
0.6%
1.887013889 1
 
0.6%
-496.1823611 1
 
0.6%
1.655555556 1
 
0.6%
1.480625 1
 
0.6%
1.349027778 1
 
0.6%
0.776875 1
 
0.6%
1.023055556 1
 
0.6%
1.302777778 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-496.1823611 1
 
0.6%
-49.77319444 1
 
0.6%
-25.90798611 1
 
0.6%
-4.976111111 1
 
0.6%
-4.195416667 1
 
0.6%
-2.930625 1
 
0.6%
-2.375486111 1
 
0.6%
-0.23 1
 
0.6%
-0.211875 1
 
0.6%
ValueCountFrequency (%)
3.443611111 1
0.6%
3.122361111 1
0.6%
3.025694444 1
0.6%
2.753708333 1
0.6%
2.744375 1
0.6%
2.74 1
0.6%
2.687604167 1
0.6%
2.671770833 1
0.6%
2.582006944 1
0.6%
2.575625 1
0.6%

calidad_viento
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
109 
155
60 
151
11 

Length

Max length3
Median length1
Mean length1.7888889
Min length1

Characters and Unicode

Total characters322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row155
2nd row155
3rd row155
4th row155
5th row155

Common Values

ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Length

2024-08-22T10:27:11.999251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-22T10:27:12.178251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 109
60.6%
155 60
33.3%
151 11
 
6.1%

Most occurring characters

ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 191
59.3%
5 131
40.7%

velocidad_max
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.02921
Minimum-999
Maximum4.9930556
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)9.4%
Memory size2.8 KiB
2024-08-22T10:27:12.373082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q12.3656771
median2.8410417
Q33.4249306
95-th percentile3.909375
Maximum4.9930556
Range1003.9931
Interquartile range (IQR)1.0592535

Descriptive statistics

Standard deviation232.37157
Coefficient of variation (CV)-4.147329
Kurtosis12.693508
Mean-56.02921
Median Absolute Deviation (MAD)0.55
Skewness-3.7982036
Sum-10085.258
Variance53996.545
MonotonicityNot monotonic
2024-08-22T10:27:12.605496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
2.690763889 1
 
0.6%
3.514513889 1
 
0.6%
-495.9194444 1
 
0.6%
3.048194444 1
 
0.6%
2.650833333 1
 
0.6%
2.422222222 1
 
0.6%
1.376180556 1
 
0.6%
1.782430556 1
 
0.6%
2.388888889 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-495.9194444 1
 
0.6%
-48.69340278 1
 
0.6%
-24.37902778 1
 
0.6%
-3.936736111 1
 
0.6%
-3.084027778 1
 
0.6%
-1.473333333 1
 
0.6%
-0.8536805556 1
 
0.6%
1.231666667 1
 
0.6%
1.272083333 1
 
0.6%
ValueCountFrequency (%)
4.993055556 1
0.6%
4.681458333 1
0.6%
4.515555556 1
0.6%
4.349027778 1
0.6%
4.18 1
0.6%
4.134583333 1
0.6%
4.075347222 1
0.6%
4.015138889 1
0.6%
3.927847222 1
0.6%
3.908402778 1
0.6%

direccion_prom
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.374823
Minimum-999
Maximum218.19236
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-08-22T10:27:12.815508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1109.08299
median136.2316
Q3159.21667
95-th percentile193.29299
Maximum218.19236
Range1217.1924
Interquartile range (IQR)50.133681

Descriptive statistics

Standard deviation265.70066
Coefficient of variation (CV)3.7755073
Kurtosis12.185079
Mean70.374823
Median Absolute Deviation (MAD)24.6125
Skewness-3.6911678
Sum12667.468
Variance70596.839
MonotonicityNot monotonic
2024-08-22T10:27:13.040388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
84.14166667 1
 
0.6%
173.2243056 1
 
0.6%
-419.5256944 1
 
0.6%
175.8034722 1
 
0.6%
208.6076389 1
 
0.6%
199.5472222 1
 
0.6%
185.8145833 1
 
0.6%
192.6840278 1
 
0.6%
191.6965278 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-419.5256944 1
 
0.6%
32.75416667 1
 
0.6%
42.34027778 1
 
0.6%
54.46111111 1
 
0.6%
57.62986111 1
 
0.6%
58.51388889 1
 
0.6%
66.35208333 1
 
0.6%
66.93194444 1
 
0.6%
66.96041667 1
 
0.6%
ValueCountFrequency (%)
218.1923611 1
0.6%
214.6555556 1
0.6%
208.6076389 1
0.6%
206.0354167 1
0.6%
204.1854167 1
0.6%
202.4833333 1
0.6%
199.5472222 1
0.6%
197.7840278 1
0.6%
195.4555556 1
0.6%
193.1791667 1
0.6%

direccion_max
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.356258
Minimum-999
Maximum202.66528
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-08-22T10:27:13.255718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1114.85885
median143.16875
Q3162.8
95-th percentile187.22639
Maximum202.66528
Range1201.6653
Interquartile range (IQR)47.941146

Descriptive statistics

Standard deviation266.46344
Coefficient of variation (CV)3.5836048
Kurtosis12.236136
Mean74.356258
Median Absolute Deviation (MAD)23.489583
Skewness-3.703159
Sum13384.126
Variance71002.767
MonotonicityNot monotonic
2024-08-22T10:27:13.482263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
101.6451389 1
 
0.6%
170.4083333 1
 
0.6%
-419.0034722 1
 
0.6%
173.2416667 1
 
0.6%
201.39375 1
 
0.6%
200.6131944 1
 
0.6%
187.1798611 1
 
0.6%
189.20625 1
 
0.6%
188.1104167 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-419.0034722 1
 
0.6%
34.58958333 1
 
0.6%
45.12152778 1
 
0.6%
58.35555556 1
 
0.6%
58.8625 1
 
0.6%
64.87361111 1
 
0.6%
67.05972222 1
 
0.6%
67.57013889 1
 
0.6%
67.90416667 1
 
0.6%
ValueCountFrequency (%)
202.6652778 1
0.6%
201.39375 1
0.6%
200.6131944 1
0.6%
198.9680556 1
0.6%
198.5291667 1
0.6%
197.2097222 1
0.6%
195.1395833 1
0.6%
189.20625 1
0.6%
188.1104167 1
0.6%
187.1798611 1
0.6%

Interactions

2024-08-22T10:27:04.147356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:51.551802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.089730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.454995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.865093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.153320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.529161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.982877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.330021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.638486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.292910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:51.733203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.227778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.587778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.995039image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.290402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.787612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.125005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.460807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.775683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.433380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:51.874801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.363893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.730428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.132426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.437160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.930278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.265840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.599767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.917392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.561523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.175417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.500627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.853770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.256122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.566262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.057542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.394518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.724698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.050832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.691945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.307325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.635620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.087562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.380112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.697978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.178771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.522639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.852508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.335746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.833565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.446685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.776074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.223861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.520413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.836900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.315329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.661641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.994115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.474312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.970431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.577752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:53.908155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.349652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.640009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.973684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.454770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.789133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.119622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.603158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:05.100193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.704344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.040253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.475285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.767821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.111980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.582795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:00.917915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.244281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.729437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:05.233274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.829808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.174590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.603655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:56.892670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.246781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.723142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.056187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.366247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:03.866540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:05.369864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:52.958941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:54.313468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:55.733050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:57.023832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:58.388087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:26:59.855591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:01.196977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:02.504793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-22T10:27:04.006354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-08-22T10:27:05.590464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-22T10:27:05.959958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

mesdiapm25calidad_pm25codigoserialtemperaturacalidad_temperaturahumedadcalidad_humedadpresioncalidad_presionp1calidad_precipitacionvelocidad_promcalidad_vientovelocidad_maxdireccion_promdireccion_max
02130.0833331.02821.20336815584.5154441550.00001550.03158311.9114581552.69076484.141667101.645139
12236.0416671.02821.54819415582.7764381550.00001550.04209011.5685831552.275486158.501389172.629861
22316.1666671.02820.82843115583.7955491550.00001550.02313911.3688401552.092778154.681944161.095833
32423.4583331.02820.93809015581.3454441550.00001550.00179211.1571251551.656389162.447222177.250694
42529.6666671.02820.91945815580.4952641550.00001550.00152111.2501531552.090069189.303472195.139583
52662.3750001.02821.43740315579.1733401550.00001550.00000011.5573891552.386250160.106944173.673611
62715.7500001.02821.09285415577.957590155-1.3875155-1.38740310.7385971551.693264100.135417116.951389
7288.2083331.02821.22233315583.4404721550.0000155-6.24370812.6717711553.75840382.16944493.245139
82914.8333331.02822.63290315578.1585001550.00001550.00000012.5756251553.71819490.926389102.394444
921018.0833331.02823.94538915573.5895691550.00001550.00000712.3104931553.459514108.813194129.338194
mesdiapm25calidad_pm25codigoserialtemperaturacalidad_temperaturahumedadcalidad_humedadpresioncalidad_presionp1calidad_precipitacionvelocidad_promcalidad_vientovelocidad_maxdireccion_promdireccion_max
503223779.2356161.08623.666250159.2419441848.5805561-1.38750010.03756911.231667175.651389168.935417
513234202.6919751.08624.466528166.7749311851.53701410.00000012.01291713.691806195.455556175.600000
523244197.2136501.08624.469722165.3675001851.03055610.00000011.71166713.078056146.755556144.386111
5332527.4020621.08624.338403161.3062501851.54166710.00006911.56159712.834583168.268056158.643056
543267524.0748921.08623.391319158.6218751850.7584031-0.69375011.06090312.531111172.701389163.200000
5532740.5130671.08623.061111164.5147221852.78118110.00000711.75340313.166944214.655556198.529167
5632834.3033461.08624.025278163.4824311852.81409710.00000011.97770813.635903175.940972171.405556
57329-389.2060041.08623.458819165.0102081852.44645810.00003511.92958313.559375174.241667173.286806
5833033.9112461.08623.629167165.6434031851.85409710.00000011.95277813.571667169.008333168.874306
5933141.0633251.08621.602708178.1039581852.68187510.00133311.45868112.623889139.196528142.368056